Functional simulation using binary decision diagrams
ICCAD '97 Proceedings of the 1997 IEEE/ACM international conference on Computer-aided design
Multi-Objective Optimization Using Evolutionary Algorithms
Multi-Objective Optimization Using Evolutionary Algorithms
Conflicting Criteria in Embedded System Design
IEEE Design & Test
Combining convergence and diversity in evolutionary multiobjective optimization
Evolutionary Computation
Multiobjective Optimization Using Evolutionary Algorithms - A Comparative Case Study
PPSN V Proceedings of the 5th International Conference on Parallel Problem Solving from Nature
An Evolutionary Approach to Hardware/ Software Partitioning
PPSN IV Proceedings of the 4th International Conference on Parallel Problem Solving from Nature
SAT-Based Techniques in System Synthesis
DATE '03 Proceedings of the conference on Design, Automation and Test in Europe - Volume 1
CHARMED: A Multi-Objective Co-Synthesis Framework for Multi-Mode Embedded Systems
ASAP '04 Proceedings of the Application-Specific Systems, Architectures and Processors, 15th IEEE International Conference
PISA: a platform and programming language independent interface for search algorithms
EMO'03 Proceedings of the 2nd international conference on Evolutionary multi-criterion optimization
Performance assessment of multiobjective optimizers: an analysis and review
IEEE Transactions on Evolutionary Computation
IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems
A SystemC-based design methodology for digital signal processing systems
EURASIP Journal on Embedded Systems
Hi-index | 0.00 |
This paper will propose a novel approach in combining Evolutionary Algorithms with symbolic techniques in order to improve the convergence of the algorithm in the presence of large search spaces containing only few feasible solutions. Such problems can be encountered in many real-world applications. Here, we will use the example of design space exploration of embedded systems to illustrate the benefits of our approach. The main idea is to integrate symbolic techniques into the Evolutionary Algorithm to guide the search towards the feasible region. We will present experimental results showing the advantages of our novel approach.